Great Expectations Employee Directory

Many data teams struggle to maintain up-to-date data documentation. Great Expectations solves this problem by rendering Expectations directly into clean, human-readable documentation. Since docs are rendered from tests, and tests are run against new data as it arrives, your documentation is guaranteed to never go stale. Additional renderers allow G... reat Expectations to generate other type of "documentation", including slack notifications, data dictionaries, customized notebooks, etc. Wouldn't it be great if your tests could write themselves? Run your data through one of Great Expectations' data profilers and it will automatically generate Expectations and data documentation. Profiling provides the double benefit of helping you explore data faster, and capturing knowledge for future documentation and testing. Automated profiling doesn't replace domain expertiseyou will almost certainly tune and augment your auto-generated Expectations over timebut it's a great way to jump start the process of capturing and sharing domain knowledge across your team. Expectations are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? How do you notify team members and triage when data validation fails? Great Expectations supports all of these use cases out of the box. Instead of building these components for yourself over weeks or months, you will be able to add production-ready validation to your pipeline in a day. This Expectations on rails framework plays nice with other data engineering tools, respects your existing name spaces, and is designed for extensibility. Every component of the framework is designed to be extensible: Expectations, storage, profilers, renderers for documentation, actions taken after validation, etc. This design choice gives a lot of creative freedom to developers working with Great Expectations.Read More